Technology Forecast

Transport sector has an important contribution on global carbon emission. In EU, Transport sector is the second most greenhouse gases emitting sector with 24.3%. Therefore, major car manufacturing countries have declared special regulations and objectives in order to decrease these high emission ratios. EU regulation requires fleets to have 95 g CO2/km cap by 2020. US and Japan has also challenging targets. These targets can only be achieved by partial introduction of electric vehicles to fleets. For this reason, most major manufacturers have already introduced their electric vehicle cars, and they have plans to develop further.

The countries have set some objectives to achieve for electric vehicle market. However, in most cases, these objectives are revised when the deadlines come closer. In 2011 US has put an objective of reaching 1 million electric vehicles by 2015. However, the total of all the electric vehicles according to the report of IEA in 2015 is 665,000. The numbers and range is also very different between different research companies. 2020 estimation for market share of electric vehicles changes from 2% to 25% according to different research organizations.

An important reason for such wide range of estimation and discrepancies on achievement of objectives are due to the major bottlenecks for electric vehicle introduction to the market. Main technical road block is the battery technology. A 24 Kwh Li_Ion Battery for around 100 miles range for a compact vehicle, costs around 8,400 $ with a weight of around 200 kgs. Charging time is also much above of that customers are used to for petrol powered vehicles. Another major road block is charging infrastructure and smart grid systems, which is also in a way related to the battery technology.

In order to estimate the future of electric vehicles, it is necessary to estimate future of electric vehicle batteries. In this article an attempt will be made to estimate the future cost and main performance specifications of electric vehicle batteries. Then an estimation regarding the possible sales volumes of electric vehicles could be done in a more reliable manner.

Electric Vehicle (EV) battery technologies is a limiting factor for the wide spread diffusion of electric vehicles. EV battery’s energy density compared to fossil fuels is still very low, thus EV’s have still stringent driving range with voluminous, heavy and high cost batteries. Automotive OEM’s are trying to estimate the future of batteries to do their plans related to electric vehicle manufacturing. This article attempts to estimate the future of EV batteries and mainly that of Li_Ion, Li_S and Li_Air Technologieswhich seem to be the most promising Technologies as of today. The article explains in detail the methodology used, and the results with an estimation of future EV market as a result of the EV battery development time scale.

Estimating the future of a new technology is not an easy task. In the past there has been many examples of gravely wrong technology forecasts. A typical example was the estimation of electronic computers future around 1940’s by some prominent scientists in US and UK. They forecasted that electronic computers would be used by only mathematicians and both countries would need only a few of them. Such problems has increased the interest on the methodology for technology forecasting.

Technology development is a discontinuous process. For this reason, forecasting is to be done with extensive and detailed analysis. Martino in his article in 1987 has classified technology forecasting methods to four “pure types” as extrapolation, leading indicators, causal models, and stochastic methods. In his article of 2003 Martino has investigated recent advances in technology forecasting and also pointed out methodologies like development of scenarios, Delphi and influence of chaos theory.

Delphi is the oldest technology forecasting method developed by RAND technologies at around 1950’s. For Delphi methodology, an expert management group is selected. This group selects the experts’ team on the subject. Prepares the survey questions. Contacts the experts and gets the answers for the survey. Analyses the results, conduct a second iteration and if necessary a third. Then writes the report analyzing the results of the iteration as well. The success of this methodology depends very much on the selection of the experts, and how much they are ready to share the information. The responses of the experts are weighted according to the different criteria and a probabilistic result is obtained.

Extrapolation methodology is an analytical method. Several performance indicators can be taken to develop a model, like performance of the technology level, number of patents, number of articles written etc. in line with the development stage. A model is fitted to the historical data and the projection of that model gives the future projection. Selection of the right extrapolation methodology is very important for the success of the forecast. If a wrong model is selected the results can be misleading. Logistic Pearl and Gompertz are the most commonly used growth curves. Steurer has used Generalized Extreme Value (GEV) which includes Gompertz as a special case and showed that for some data improved the flexibility of S-curve.